Ergonomic assessment of human posture plays a vital role in understanding work-related safety and health. Current posture estimation approaches face occlusion challenges in teleoperation and physical human-robot interaction. We investigate if the leader robot is an adequate sensor for posture estimation in teleoperation and we introduce a new probabilistic approach that relies solely on the trajectory of the leader robot for generating observations. We model the human using a redundant, partially-observable dynamical system and we infer the posture using a standard particle filter. We compare our approach with postures from a commercial motion capture system and also two least-squares optimization approaches for human inverse kinematics. The results reveal that the proposed approach successfully estimates human postures and ergonomic risk scores comparable to those estimates from gold-standard motion capture.
翻译：人类姿势的地理学评估在理解与工作有关的安全和健康方面发挥着至关重要的作用。当前姿势估计方法在远程操作和人体-机器人物理互动方面面临着排斥性挑战。我们调查领先机器人是否足以在远程操作中进行姿势估计的传感器,我们采用新的概率性方法,完全依靠领先机器人的轨迹进行观测。我们用一个多余的、部分可观测的动态系统模拟人类,用标准粒子过滤器推断姿态。我们比较我们的方法与商业动作捕捉系统的姿态以及人类反动力学两种最差的优化方法。结果显示,拟议方法成功地估计了人类姿势和人类基因学风险的得分,与从黄金-标准运动捕捉中得出的估计相近。
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